Gast ::
| Hauptseite > Publikationsdatenbank > RPEE-Heads: A Novel Benchmark For Pedestrian Head Detection in Crowd Videos > print |
| Hauptseite > Publikationsdatenbank > RPEE-Heads: A Novel Benchmark For Pedestrian Head Detection in Crowd Videos > print |
| 001 | 1034454 | ||
| 005 | 20241223113912.0 | ||
| 024 | 7 | _ | |2 doi |a 10.48550/arXiv.2411.18164 |
| 037 | _ | _ | |a FZJ-2024-07220 |
| 041 | _ | _ | |a English |
| 088 | _ | _ | |2 arXiv |a https://doi.org/10.48550/arXiv.2411.18164 |
| 100 | 1 | _ | |0 P:(DE-HGF)0 |a Abubaker, Mohammed |b 0 |
| 245 | _ | _ | |a RPEE-Heads: A Novel Benchmark For Pedestrian Head Detection in Crowd Videos |
| 260 | _ | _ | |b arXiv |c 2024 |
| 336 | 7 | _ | |0 PUB:(DE-HGF)25 |2 PUB:(DE-HGF) |a Preprint |b preprint |m preprint |s 1734595884_7058 |
| 336 | 7 | _ | |2 ORCID |a WORKING_PAPER |
| 336 | 7 | _ | |0 28 |2 EndNote |a Electronic Article |
| 336 | 7 | _ | |2 DRIVER |a preprint |
| 336 | 7 | _ | |2 BibTeX |a ARTICLE |
| 336 | 7 | _ | |2 DataCite |a Output Types/Working Paper |
| 520 | _ | _ | |a The automatic detection of pedestrian heads in crowded environments is essential for crowd analysis and management tasks, particularly in high-risk settings such as railway platforms and event entrances. These environments, characterized by dense crowds and dynamic movements, are underrepresented in public datasets, posing challenges for existing deep learning models. To address this gap, we introduce the Railway Platforms and Event Entrances-Heads (RPEE-Heads) dataset, a novel, diverse, highresolution, and accurately annotated resource. It includes 109,913 annotated pedestrian heads across 1,886 images from 66 video recordings, with an average of 56.2 heads per image. Annotations include bounding boxes for visible head regions. In addition to introducing the RPEE-Heads dataset, this paper evaluates eight state-of-the-art object detection algorithms using the RPEE-Heads dataset and analyzes the impact of head size on detection accuracy. The experimental results show that You Only Look Once v9 and Real-Time Detection Transformer outperform the other algorithms, achieving mean average precisions of 90.7% and 90.8%, with inference times of 11 and 14 milliseconds, respectively. Moreover, the findings underscore the need for specialized datasets like RPEE-Heads for training and evaluating accurate models for head detection in railway platforms and event entrances. The dataset and pretrained models are available at https://doi.org/10.34735/ped.2024.2. |
| 536 | _ | _ | |0 G:(DE-HGF)POF4-5111 |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |0 G:(BMBF)01DH16027 |a Pilotprojekt zur Entwicklung eines palästinensisch-deutschen Forschungs- und Promotionsprogramms 'Palestinian-German Science Bridge' (01DH16027) |c 01DH16027 |x 1 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |2 Other |a Computer Vision and Pattern Recognition (cs.CV) |
| 650 | _ | 7 | |2 Other |a Machine Learning (cs.LG) |
| 650 | _ | 7 | |2 Other |a FOS: Computer and information sciences |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Alsadder, Zubayda |b 1 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Abdelhaq, Hamed |b 2 |e Corresponding author |
| 700 | 1 | _ | |0 P:(DE-Juel1)132064 |a Boltes, Maik |b 3 |e Corresponding author |u fzj |
| 700 | 1 | _ | |0 P:(DE-Juel1)185971 |a Alia, Ahmed |b 4 |u fzj |
| 773 | _ | _ | |a 10.48550/arXiv.2411.18164 |
| 856 | 4 | _ | |u https://arxiv.org/abs/2411.18164 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1034454 |p VDB |
| 910 | 1 | _ | |0 I:(DE-588b)5008462-8 |6 P:(DE-Juel1)132064 |a Forschungszentrum Jülich |b 3 |k FZJ |
| 910 | 1 | _ | |0 I:(DE-588b)5008462-8 |6 P:(DE-Juel1)185971 |a Forschungszentrum Jülich |b 4 |k FZJ |
| 913 | 1 | _ | |0 G:(DE-HGF)POF4-511 |1 G:(DE-HGF)POF4-510 |2 G:(DE-HGF)POF4-500 |3 G:(DE-HGF)POF4 |4 G:(DE-HGF)POF |9 G:(DE-HGF)POF4-5111 |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |v Enabling Computational- & Data-Intensive Science and Engineering |x 0 |
| 914 | 1 | _ | |y 2024 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-7-20180321 |k IAS-7 |l Zivile Sicherheitsforschung |x 0 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-7-20180321 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|